1 The Corpus

The maps and analyses in this section are based on a corpus of 79,040 studies in the published scientific literature. The corpus is shared between chapter 3 and 4 of the values assessment. For more information about the corpus see IPBES VA Chapter 3. Systematic review on Method Families (https://doi.org/10.5281/zenodo.4404436) and IPBES VA Chapter 4. Systematic review on valuation uptake (https://doi.org/10.5281/zenodo.4391335).

2 Georeferencing the Corpus

To identify country names in the corpus of literature a two step approach was used. First, we wanted to understand where studies were conducted and searched the title, abstract, and keywords of each paper for country names. Second, to understand where the funding organizations were located we searched the affiliations, acknowledgments, and funding text for country names.

The input data we used are the following:

The python code used to georeference the corpus can be found here. An overview of the pipeline is provided in the following schematic and described below.

knitr::include_graphics("pilot2.svg")
Overview of the process of Georeferencing the corpus of valuation studies

Overview of the process of Georeferencing the corpus of valuation studies

Step 1: Extract country names from text Country names were extracted from the title, abstract, and keywords of each paper with a regular expression and the associated ISO code was added into a a column in the dataset. The same regular expression was also used to search the affiliations, acknowledgments, and funding text of the same paper and placed into a second column.

Step 2 and 3: Bundle countries in regions and subregions The IPBES Regions and Subregions datatset was then used to add additional region and subregion attributes to the dataset by matching the ISO3 code.

Step 4: Find TS accordingly We used a set of files to add additional attributes to the dataset that identified the topics. The set of files contained identifying information for papers derived from sets of web of science searches targeting particular topics. This identifying information was then matched to the corpus, and the topic extracted. For more details on topic identification please see chapter 4 systematic review on valuation uptake (https://doi.org/10.5281/zenodo.4391335).

Finally, the complete corpus with the added attributes of country ISO codes of both funding institutions and research locations, and topic identification were used as the basis of the rest of the research project. The complete corpus can be found on Zenodo here: [https://doi.org/[INSERT](https://doi.org/%5BINSERT DOI]

3 Valuation Maps

The complete georeferenced valuation corpus was used to understand the location of valuation studies throughout the world and the location of the institutions conducting those studies.

We counted the number of times a country or territory was listed in the corpus from the column of country or territory names obtained from the title, abstract, and keywords search as a proxy for the density of valuation studies.

Separately, we counted the number of times a country or territory was listed in the corpus from the column of country or territory names obtained from the affiliations acknowledgments, and funding text as a proxy for the density of institutions.

data <- readxl::read_excel("Data/IPBES_VA_Uptake_Corpus_06May20_GEONames_TS_16June20.xlsx", sheet = 1)
n <- nrow(data)

t <- data %>% 
  mutate(x = strsplit(as.character(CountryName_TI_AB_DE_ID), ", ")) %>%
  unnest(x) %>% 
  count(x, sort = TRUE)

studies_na <- t %>% 
  filter(is.na(x)) 

t_names2 <- data %>% 
  mutate(x = strsplit(as.character(CountryName_CI_FU_FX), ", ")) %>%
  unnest(x) %>% 
  count(x, sort = TRUE)

names2_na <- t_names2 %>% 
  filter(is.na(x)) 

cat("Out of", n,"total studies,", studies_na$n, "studies did not identify a country or territory within their title, abstract, or keywords. ", "While", names2_na$n, "studies did not identify a country within their affiliations, acknowledgments, and funding text.")

Out of 79040 total studies, 30259 studies did not identify a country or territory within their title, abstract, or keywords. While 345 studies did not identify a country within their affiliations, acknowledgments, and funding text.

3.1 Geographic Distribution

We will now present the results from the analysis through a series of maps showing both the raw country or territory results and summarized by IPBES regions and subregions. No data is always displayed in grey. The darker green values represent higher density of studies, while the darker blue values represent higher density of institutions.

# Declare Functions
countFunction <- function(data, x) {
  data %>% 
    mutate(x = strsplit(as.character(x), ", ")) %>% 
    unnest(x) %>% 
    count(x, sort = TRUE) %>% 
    drop_na() %>% 
    mutate(n_log = log(n))
}

# Necessary datasets for the upcoming maps 
harmonized_data <- read.csv("Outputs/Corpus/harmonized_data.csv")
indicators <- read.csv("Outputs/indicators_compiled.csv")
column_names <- readxl::read_excel("Data/names_of_columns.xlsx")
countries <- read_sf("Data/gadm36_levels_shp/gadm36_0.shp") %>% 
  rename(ISO_Alpha_3 = GID_0)

# Combine and project data
colnames(indicators)[2:25] <- column_names$Short_Name

df <- left_join(harmonized_data, indicators, by = "ISO_Alpha_3")
df <- left_join(countries, df, by = "ISO_Alpha_3") 

# Project data 
robin_crs <- "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m"
poly <- st_transform(df, robin_crs)

3.1.1 Density of studies

All

# Preparing Data 

# all
percentage_VS_all <- st_drop_geometry(df) %>% 
  select(NAME_0, ISO_Alpha_3, Names1, Names2) %>% 
  mutate(Names1_percent = (Names1/sum(df$Names1, na.rm = T))) %>% 
  mutate(Names2_percent = (Names2/sum(df$Names2, na.rm = T))) %>% 
  arrange(desc(Names1_percent))

# Before 2010
## Create dataset 
corpus_b2010 <- data %>% 
  filter(PY < 2010)

n1 <- corpus_b2010 %>% 
  countFunction(x = corpus_b2010$CountryName_TI_AB_DE_ID) %>% 
  rename("ISO_Alpha_3" = x, "Names1" = n, "Names1_log" = n_log)

n2 <- corpus_b2010 %>% 
  countFunction(x = corpus_b2010$CountryName_CI_FU_FX) %>% 
  rename("ISO_Alpha_3" = x, "Names2" = n, "Names2_log" = n_log)

df_b2010 <- full_join(n1, n2, by = "ISO_Alpha_3")

df_b2010_t <- left_join(countries, df_b2010, by = "ISO_Alpha_3") 

## Project data 
robin_crs <- "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m"
poly_b2010 <- st_transform(df_b2010_t, robin_crs)

percentage_VS_b2010 <- st_drop_geometry(df_b2010_t) %>% 
  select(NAME_0, ISO_Alpha_3, Names1, Names2) %>% 
  mutate(Names1_percent = (Names1/sum(df_b2010_t$Names1, na.rm = T))) %>% 
  mutate(Names2_percent = (Names2/sum(df_b2010_t$Names2, na.rm = T))) %>% 
  arrange(desc(Names1_percent))

# After 2010

data_2010 <- read.csv("Outputs/Corpus_2010/harmonized_data.csv")
df_2010 <- left_join(countries, data_2010, by = "ISO_Alpha_3") 

robin_crs <- "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m"
poly_2010 <- st_transform(df_2010, robin_crs)

percentage_VS_a2010 <- st_drop_geometry(df_2010) %>% 
  select(NAME_0, ISO_Alpha_3, Names1, Names2) %>% 
  mutate(Names1_percent = (Names1/sum(df_2010$Names1, na.rm = T))) %>% 
  mutate(Names2_percent = (Names2/sum(df_2010$Names2, na.rm = T))) %>% 
  arrange(desc(Names1_percent))

# For plots
grid <- st_graticule(lat = seq(-90, 90, by = 30), # the graticules 
                     lon = seq(-180, 180, by = 60)) %>% 
  st_transform("+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m") %>% 
  st_geometry

Showcases the number of valuation studies conducted within each country or territory for the entire dataset. Please note that the scales are not consistent between maps.

cat("There were", 
    sum(t$n) - studies_na$n,
    "identifications of a country or territory from the title, abstract, or keywords, consisting of",
    nrow(t) - nrow(studies_na),
    "countries or territories identified.\n\n",
    "\nThe United States has disproportionately higher valuation studies than other countries.", " In descending order,", 
    paste(percentage_VS_all$NAME_0[1]), 
    paste("(",round(percentage_VS_all$Names1_percent[1]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_all$NAME_0[2]), 
    paste("(",round(percentage_VS_all$Names1_percent[2]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_all$NAME_0[3]), 
    paste("(",round(percentage_VS_all$Names1_percent[3]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_all$NAME_0[4]), 
    paste("(",round(percentage_VS_all$Names1_percent[4]*100, 2), "%) and", sep = ""), 
    paste(percentage_VS_all$NAME_0[5]), 
    paste("(",round(percentage_VS_all$Names1_percent[5]*100, 2), "%)", sep = ""),
    "have the highest valuation studies in total and for studies post 2010."
    )

There were 64688 identifications of a country or territory from the title, abstract, or keywords, consisting of 217 countries or territories identified.

The United States has disproportionately higher valuation studies than other countries. In descending order, United States (10.96%), China (6.66%), Australia (5.36%), Brazil (4.66%) and India (3.74%) have the highest valuation studies in total and for studies post 2010.

There was a large relative increase of valuation studies conducted in China before 2010 and after 2010. Before 2010, China comprised of 2.80% of the valuation studies, but after 2010 China comprised 8.04% of the valuation studies.

plot1 <- ggplot(poly) + # Names 1 all studies 
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names1, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_colour_manual(values = NA) +              
  guides(colour = guide_legend("No data", override.aes = list(colour = "grey", fill = "grey")))+
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom") +
  scale_fill_gradient(
    low = "#F7FCB9",
    high = "#006837",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 5, 
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Density of Studies") 

# Names 1 Log
plot1_log <- ggplot(poly) + # Names 2 log all studies
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names1_log, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_fill_gradient(
    low = "#F7FCB9",
    high = "#006837",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 5, 
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Density of Studies (log)") +
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom")

plot_grid(plot1, plot1_log, labels = "auto", ncol = 1)

Before 2010

Showcases the number of valuation studies conducted within each country or territory in the dataset before 2010. Please note that the scales are not consistent between maps.

cat("Before 2010, the countries with the highest valuation studies conducted were the", 
    paste(percentage_VS_b2010$NAME_0[1]), 
    paste("(",round(percentage_VS_b2010$Names1_percent[1]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_b2010$NAME_0[2]), 
    paste("(",round(percentage_VS_b2010$Names1_percent[2]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_b2010$NAME_0[3]), 
    paste("(",round(percentage_VS_b2010$Names1_percent[3]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_b2010$NAME_0[4]), 
    paste("(",round(percentage_VS_b2010$Names1_percent[4]*100, 2), "%) and", sep = ""), 
    paste(percentage_VS_b2010$NAME_0[5]), 
    paste("(",round(percentage_VS_b2010$Names1_percent[5]*100, 2), "%).", sep = "")
    )

Before 2010, the countries with the highest valuation studies conducted were the United States (12.21%), Australia (6.37%), United Kingdom (5.17%), India (3.61%) and Canada (3.32%).

# Plotting Names 1 
plotC <- ggplot(poly_b2010) + # Names 1 all studies before 2010
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names1, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_colour_manual(values = NA) +              
  guides(colour = guide_legend("No data", override.aes = list(colour = "grey", fill = "grey")))+
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom") +
  scale_fill_gradient(
    low = "#F7FCB9",
    high = "#006837",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 5, 
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Density of Studies") 

# Plotting Names 1 log 
plotC_log <- ggplot(poly_b2010) + # Names 2 log all studies before 2010
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names1_log, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_fill_gradient(
    low = "#F7FCB9",
    high = "#006837",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 5, 
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Density of Studies (log)") +
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom")

plot_grid(plotC, plotC_log, labels = "auto", ncol = 1)

After 2010

Showcases the number of valuation studies conducted within each country or territory in the dataset from 2010. Please note that the scales are not consistent between maps.

cat("In descending order, the", 
    paste(percentage_VS_a2010$NAME_0[1]), 
    paste("(",round(percentage_VS_a2010$Names1_percent[1]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_a2010$NAME_0[2]), 
    paste("(",round(percentage_VS_a2010$Names1_percent[2]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_a2010$NAME_0[3]), 
    paste("(",round(percentage_VS_a2010$Names1_percent[3]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_a2010$NAME_0[4]), 
    paste("(",round(percentage_VS_a2010$Names1_percent[4]*100, 2), "%) and", sep = ""), 
    paste(percentage_VS_a2010$NAME_0[5]), 
    paste("(",round(percentage_VS_a2010$Names1_percent[5]*100, 2), "%)", sep = ""),
    "have the highest valuation studies in total and for studies post 2010."
    )

In descending order, the United States (10.49%), China (8.04%), Brazil (5.18%), Australia (5.01%) and India (3.8%) have the highest valuation studies in total and for studies post 2010.

# Plotting Names 1 
plotA <- ggplot(poly_2010) + # Names 1 all studies 
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names1, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_colour_manual(values = NA) +              
  guides(colour = guide_legend("No data", override.aes = list(colour = "grey", fill = "grey")))+
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom") +
  scale_fill_gradient(
    low = "#F7FCB9",
    high = "#006837",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 5, 
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Density of Studies") 

# Plotting Names 1 log 
plotA_log <- ggplot(poly_2010) + # Names 2 log all studies
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names1_log, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_fill_gradient(
    low = "#F7FCB9",
    high = "#006837",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 5, 
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Density of Studies (log)") +
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom")

plot_grid(plotA, plotA_log, labels = "auto", ncol = 1)

3.1.2 Density of institutions

All

Showcases the number of studies which reference the particular country within the affiliations, acknowledgments, or funding text for the entire dataset as a proxy for institutions which are conducing the research. Please note that the scales are not consistent between maps.

percentage_VS_all <- percentage_VS_all %>% 
  arrange(desc(Names2_percent))

cat("There were", 
    sum(t_names2$n) - names2_na$n,
    "identifications of a country or territory from the affiliations, acknowledgments, or funding text, consisting of",
    nrow(t_names2) - nrow(names2_na),
    "countries or territories.\n\n",
    "\nThe United States and the United Kingdom (UK) have disproportionately higher density of institutions than other countries. In descending order, the",
    paste(percentage_VS_all$NAME_0[1]), 
    paste("(",round(percentage_VS_all$Names2_percent[1]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_all$NAME_0[2]), 
    paste("(",round(percentage_VS_all$Names2_percent[2]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_all$NAME_0[3]), 
    paste("(",round(percentage_VS_all$Names2_percent[3]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_all$NAME_0[4]), 
    paste("(",round(percentage_VS_all$Names2_percent[4]*100, 2), "%) and", sep = ""), 
    paste(percentage_VS_all$NAME_0[5]), 
    paste("(",round(percentage_VS_all$Names2_percent[5]*100, 2), "%)", sep = ""),
    "have the highest density of institutions."
    )

There were 140188 identifications of a country or territory from the affiliations, acknowledgments, or funding text, consisting of 210 countries or territories.

The United States and the United Kingdom (UK) have disproportionately higher density of institutions than other countries. In descending order, the United States (17.89%), United Kingdom (7.36%), Germany (5.19%), Australia (4.9%) and China (4.64%) have the highest density of institutions.

plot2 <- ggplot(poly) + # Names 2 all studies
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names2, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_fill_gradient(
    low = "#DEEBF7",
    high = "#08519C",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 5, 
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Density of Institutions") +
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom")

# Names 2 Log
plot2_log <- ggplot(poly) + # Names 2 log all studies
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names2_log, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_fill_gradient(
    low = "#DEEBF7",
    high = "#08519C",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 5, 
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Density of Institutions (log)") +
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom")

plot_grid(plot2, plot2_log, labels = "auto", ncol = 1)

Before 2010

Showcases the number of studies which reference the particular country within the affiliations, acknowledgments, or funding text for the studies in the dataset before 2010 as a proxy for institutions which are conducing the research in this time period. Please note that the scales are not consistent between maps.

percentage_VS_b2010 <- percentage_VS_b2010 %>% 
  arrange(desc(Names2_percent))

cat("Before 2010, in descending order, the",
    paste(percentage_VS_b2010$NAME_0[1]), 
    paste("(",round(percentage_VS_b2010$Names2_percent[1]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_b2010$NAME_0[2]), 
    paste("(",round(percentage_VS_b2010$Names2_percent[2]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_b2010$NAME_0[3]), 
    paste("(",round(percentage_VS_b2010$Names2_percent[3]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_b2010$NAME_0[4]), 
    paste("(",round(percentage_VS_b2010$Names2_percent[4]*100, 2), "%) and", sep = ""), 
    paste(percentage_VS_b2010$NAME_0[5]), 
    paste("(",round(percentage_VS_b2010$Names2_percent[5]*100, 2), "%)", sep = ""),
    "have the highest density of institutions."
    )

Before 2010, in descending order, the United States (25.28%), United Kingdom (9.52%), Canada (5.23%), Australia (5.03%) and Germany (4.83%) have the highest density of institutions.

# Plotting Names 2 
plotD <- ggplot(poly_b2010) + # Names 2 all studies before 2010
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names2, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_fill_gradient(
    low = "#DEEBF7",
    high = "#08519C",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 5, 
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Density of Institutions") +
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom")

# Plotting Names 2 log
plotD_log <- ggplot(poly_b2010) + # Names 2 log all studies before 2010
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names2_log, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_fill_gradient(
    low = "#DEEBF7",
    high = "#08519C",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 5, 
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Density of Institutions (log)") +
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom")

plot_grid(plotD, plotD_log, labels = "auto", ncol = 1)

After 2010

Showcases the number of studies which reference the particular country or territory within the affiliations, acknowledgments, or funding text for the studies in the dataset from 2010 as a proxy for institutions which are conducing the research in this time period. Please note that the scales are not consistent between maps.

percentage_VS_a2010 <- percentage_VS_a2010 %>% 
  arrange(desc(Names2_percent))

cat("After 2010, in descending order, the",
    paste(percentage_VS_a2010$NAME_0[1]), 
    paste("(",round(percentage_VS_a2010$Names2_percent[1]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_a2010$NAME_0[2]), 
    paste("(",round(percentage_VS_a2010$Names2_percent[2]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_a2010$NAME_0[3]), 
    paste("(",round(percentage_VS_a2010$Names2_percent[3]*100, 2), "%),", sep = ""), 
    paste(percentage_VS_a2010$NAME_0[4]), 
    paste("(",round(percentage_VS_a2010$Names2_percent[4]*100, 2), "%) and", sep = ""), 
    paste(percentage_VS_a2010$NAME_0[5]), 
    paste("(",round(percentage_VS_a2010$Names2_percent[5]*100, 2), "%)", sep = ""),
    "have the highest density of institutions."
    )

After 2010, in descending order, the United States (15.81%), United Kingdom (6.75%), China (5.39%), Germany (5.29%) and Australia (4.87%) have the highest density of institutions.

plotB <- ggplot(poly_2010) + # Names 2 all studies
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names2, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_fill_gradient(
    low = "#DEEBF7",
    high = "#08519C",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 5, 
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Density of Institutions") +
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom")

# Plotting Names 2 log
plotB_log <- ggplot(poly_2010) + # Names 2 log all studies
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names2_log, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_fill_gradient(
    low = "#DEEBF7",
    high = "#08519C",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 5, 
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Density of Institutions (log)") +
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom")

plot_grid(plotB, plotB_log, labels = "auto", ncol = 1)

3.1.3 Summarized by region

Europe and Central Asia region has the highest density of institutions, but comes in third for the highest density of studies. In contrast, the Americas region comes in first for the highest density of studies and second for institutions.

Besides Antarctica, Africa has the lowest density of studies and institutions.

Density of studies

knitr::include_graphics("Outputs/Maps/Names1_perregion.png")

region1_counts <- data %>% 
  mutate(x = strsplit(as.character(Region_TI_AB_DE_ID), ", ")) %>%
  unnest(x) %>% 
  count(x, sort = TRUE)

region1_na <- region1_counts %>% 
  filter(is.na(x)) 

cat("* ",region1_na$n, "studies did not have a region identified for the density of studies.")
  • 30428 studies did not have a region identified for the density of studies.

Density of institutions

knitr::include_graphics("Outputs/Maps/Names2_perregion.png")

  • 376 studies did not have a region identified for the density of institutions

3.1.4 Summarized by subregion

Europe and North America disproportionately have the highest density of studies and institutions between subregions, while Central Asia have the lowest density for both categories.

Density of studies

knitr::include_graphics("Outputs/Maps/Names1_persubregion.png")

subregion1_counts <- data %>% 
  mutate(x = strsplit(as.character(Subregion_TI_AB_DE_ID), ", ")) %>%
  unnest(x) %>% 
  count(x, sort = TRUE)

subregion1_na <- subregion1_counts %>% 
  filter(is.na(x)) 

cat("* ",subregion1_na$n, "studies did not have a subregion identified for the density of studies.")
  • 30428 studies did not have a subregion identified for the density of studies.

Density of institutions

knitr::include_graphics("Outputs/Maps/Names2_persubregion.png")

  • 376 studies did not have a subregion identified for the density of institutions

3.2 Relationships between density of studies and institutions

OECD member countries and middle-income countries broadly had relatively more studies in their countries relative to research and funding affiliations; also Greenland, the Antarctic, some Latin American and Asian, most African countries had relatively more research and funding affiliations compared to the number of studies in their countries. Countries mostly in the southern hemisphere had a disproportionate number of researchers engaged in ecosystem services/NCP assessment studies, relative to the number of studies carried out in their country, in comparison to Northern hemisphere countries. Because these are ratios, it is better to consider them in the light of country/population size. From a Southern-country perspective, this could indicate: (i) high frequency of collaboration outside their own countries of researchers from countries in the South, and (ii) underfunding of domestic valuation studies in countries of the South relative to the number of domestic researchers and funding agencies. From the perspective of countries in the North this could indicate (i) relatively limited research collaboration outside their country for researchers from the North, and (ii) well-funded ecosystem services/NCP research relative to number of researchers and funding affiliations. From both these perspectives the narrative is of a relative export of ecosystem services/NCP research capacity from Southern countries to other countries. The data may also simply show a relative deficit of ecosystem services/NCP study applications in the South relative to the North. No data was found on cross-border flows of funding or researchers to make any further interpretation. This is a potential knowledge gap. More analysis is needed on whether there are patterns in relation to ecosystem assessment method families, and whether the patterns have changed over time. There are many exceptions to the broad global pattern which merit country-specific studies of the institutional context of ecosystem assessment to identify barriers and successful leverage points.

plot_1and2_ratio <- ggplot(poly) + # Names 2 log all studies
  geom_sf(data = grid, 
          colour = "gray60",
          linetype = "dashed") +
  geom_sf(aes(fill = Names1/Names2, colour = NULL)) +
  annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
  annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
  annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
  annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
  annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
  annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
  annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
  annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
  annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
  annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
  scale_fill_gradient(
    low = "#F7FCB9",
    high = "#FB8C00",
    space = "Lab",
    na.value = "grey",
    aesthetics = "fill",
    n.breaks = 6, 
    breaks = c(1,3,5,7,9,11),
    guide = guide_colorbar(title.position = "top",
                           title.hjust = .5,
                           barwidth = 10, 
                           barheight = 0.5
    )) +
  labs(fill = "Ratio of Studies and Institutions") +
  theme(panel.background = element_blank(), 
        axis.text.x = element_text(size = 12),
        axis.title = element_blank(),
        legend.position = "bottom")

plot_1and2_ratio

It is clear that there is a significant positive linear relationship between the density of studies and institutions. It is important to note that there were many more identified countries for institutions than location of studies.

knitr::include_graphics("Outputs/Corpus/Names1_Names2.png")
Density of studies vs density of institutions

Density of studies vs density of institutions

4 Indicator Analysis

The IPBES Core Indicators were used alongside a chosen set of other relevant indicators to understand geographic trends between density of valuation studies and how they relate to biological and socioeconomic indicators.

4.1 Selection of Indicators

We used all the most recent year of the IPBES Core Indicators available within the country dataset except for two indicators, Countries/Regions with Active NBSAP and Category 1 nations in CTIES, as these are binary in the dataset and would not be compatible with the following analysis. We selected a specific category from the indicators with multiple categories. For example, for the indicator “Area of forest production under FSC and PEFC certification” we chose the FSC certification area and not the PEFC certification area.

A set of other indicators were included in the analysis to expand the coverage of socioeconomic variables. We included the human development index (HDI), average harmonized learning outcomes score, gross domestic product (GDP), corruption perception index (CPI), and population.

These datasets were downloaded, cleaned, and had ISO3 codes added to easily merge them into the analysis. The latest data available was used for each indicator.

Here is the table of all of the indicators used, the category selected, the year the data is from, and the number assigned to them.

Table 1: Details of the Indicators Used
Name Category Year
Forest Area Under FSC and PEFC Certification FSC_area 2016
Biodiversity Habitat Index Average 2014
Biodiversity Intactness Index Value 2005
Biocapacity Per Capita Value - Total 2012
Ecological Footprint Per Capita Value - Total 2012
Forest Area Forest area (1000ha) 2015
Water Footprint Water Footprint - Total (Mm3/y) 2013
Inland Fishery Production Capture 2015
Region-based Marine Trophic Index 1950 2014
Nitrogen + Phosphate Fertilizers N total nutrients - Consumption in nutrients 2014
Percent Nitrogen Use Efficiency Nitrogen Use Efficiency (%) 2009
Percentage and Total Area Covered by Protected Areas Terrestrial - Protected Area (%) 2017
Percentage of Undernourished People Prevalence of undernourishment (%) (3-year average) 2015
Proportion of Local Breeds Classified as Being At Risk, Not-at-risk or Unknown Level of Risk of Extinction At Risk of Extinction 2016
Percentage of Key Biodiverisy Areas Protected Estimate 2016
Protected Area Management Effectiveness PA Assessed on Management Effectiveness (%) 2015
Protected Area Connectedness Index Protected Area Connectedness Index 2012
Species Habitat Index Species Habitat Index 2014
Species Protection Index (%) Species Protection Index (%) 2014
Species Status Information Index Value 2014
Total Wood Removals (roundwood, m3) Total 2014
Trends in Forest Extent (Tree Cover) Percentage of Tree Cover Loss 2015
Nitrogen Deposition Trends (kg N/ha/yr) Nitrogen Deposition Trends (kg N/ha/yr) 2030
Trends in Pesticides Use Use of pesticides (3-year average) 2013
Human Development Index NA 2018
Average Harmonized Learning Outcomes Score NA 2015
Gross Domestic Product (GDP) NA 2019
Corruption Perception Index NA 2020
Population NA 2018

There were a few instances of duplicated values which were double checked with the original dataset and the erroneous value removed. Examples include having two values for USA due to the separation of Hawaii in the original dataset. In these cases Hawaii was removed and the value referring to the rest of the states of the country was used instead. Additionally, Indicator 9, Region-based Marine Trophic Index, the mean of the regions was calculated per country, as countries such as Germany have multiple regions with distinct values.

4.2 Dataset Compilation

To understand how valuation is spread across geographies, we counted the number of times each country’s or territories’ ISO code appeared in the corpus for both geography columns added in step 2. The result is the density of studies per country or territory and the density of funding institutions per country or territory for the entire corpus.

The external indicators were also joined onto the dataset to analyze the relationships between these socioeconomic indicators and the density of studies and funding institutions.

This process was also repeated with an additional filter that excluded any studies published before 2010.

4.3 Pearson Correlation Analysis

To investigate the relationships between indicators and the number of valuation studies, we ran a pearson correlation analysis. The statistical analysis calculated the trends between the number of studies in each country or territory (Density of studies) and the number of studies funded in each country or territory (Density of institutions) and each of the indicators. The results are shown below.

Insignificant relationships are blank, significant relationships (P < 0.01) are shown with circles. The strength of the correlation corresponds to the size of the circle and the color represents positive (blue) or negative (red) trends.

Description of results

There is a very strong positive relationship between GDP and the density of both studies and institutions. GDP per capita does not exhibit the same strong correlation.

Density of studies is also strongly positively correlated to total wood removals, trends in pesticide use, population, nitrogen fertilizers, and water footprint index.

Density of institutions is also strongly positively correlated with total wood removals, and nitrogen fertilizers.

While most significant relationships are positive, there are two negative relationships present. Biodiversity inexactness index is negatively correlated with both the density of studies and institutions, and protected area management effectiveness is slightly negative correlated with the density of institutions.

knitr::include_graphics("Outputs/Pearson_correlation_table/correlation_figure.png")
Pearson correlations of geographic valuation studies and indicators

Pearson correlations of geographic valuation studies and indicators

The same pearson correlation analysis with the indicators was conducted on the log of the number of studies in each country or territory and the log of the number of studies funded from each country.

Description of results

Generally, the log density of studies and institutions show stronger and more correlations with the indicators. Within the log relationship, the protected area management effectiveness becomes significantly negatively correlated with both the density of studies and institutions. The biodiversity inexactness index also follows the same pattern.

For the log density of studies compared to the non-transformed density of studies, GDP becomes less strongly correlated, local breeds at risk of extinction becomes positively correlated, protected area connectedness index becomes strongly positively correlated, species protection index becomes positively correlated, as well as trends in forest extent, and nitrogen deposition trends.

For the log density of institutions compared to the non-transformed density of institutions, the human development index, average harmonized learning outcomes, corruption perception index, population, and local breeds at risk of extinction all become more strongly positively correlated, while the GDP becomes slightly less positively correlated. Additionally, the percentage of undernourished people becomes negatively correlated with the log density of studies, the species protection index and the species status information index become positively correlated, and finally the relationship to trends in pesticide use is no longer significant.

For both the log density of studies and institutions, the protected area connected index becomes strongly positively corrrelated.

knitr::include_graphics("Outputs/Pearson_correlation_table/correlation_figure_log.png")
Pearson correlations of log geographic valuation studies and the indicators

Pearson correlations of log geographic valuation studies and the indicators

4.4 Individual Trend Analysis

The individual trends between each indicator and the number of studies and number of funding institutions are shown here for the entire corpus. For each indicator, there are two four panel figures with the top figure showing the trends with the raw values, and the bottom figure displaying the trends with log transformed x-axis values.

The dots represent countries within the dataset that have values for both the indicator and valuation atlas. The associated p value of the linear trend model is shown in the corner of the image in red. The trend line is shown in blue and the associated standard error in grey. Please note that the y axis is not necessarily consistent between the four boxes and may include values that aren’t actually found in the data to showcase the full extent of the standard error of the trend line.

There are some broad narratives emerge from this analysis:

  • Fewer studies are being carried out in countries with extreme poverty.

  • More ecosystem service/NCP assessment studies are being carried out where biodiversity and environmental degradation is higher and protection lower.

  • More studies are being carried out where natural capital is the highest and the conservation discourse is more advanced.

Some contradictions between these single variable narratives exist, such as more ecosystem services/NCP assessments carried out in countries with a lower protected area effectiveness index, and in countries with a higher species protection index, while these are significantly negatively correlated across countries. There is a knowledge gap and no robust understanding can be concluded on this.

For each figure the panels are arranged accordingly:

  • Density of studies (top-left) is the number of valuation studies for each country or territory in the corpus

  • Density of studies from 2010 (top-right) is the number of valuation studies for each country or territory in the corpus from 2010 to 2020

  • Density of institutions (bottom-left) is the number of valuation studies funded from each country or territory in the corpus

  • Density of institutions from 2010 (bottom-right) is the number of valuation studies funded from each country or territory in the corpus from 2010 to 2020

Human Development Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/HDI.svg")
Relationships between density of valuation studies and associated institutions, and the human development index

Relationships between density of valuation studies and associated institutions, and the human development index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/HDI_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the human development index

Log relationships between density of valuation studies and associated insitutions, and the human development index

Average Harmonized Learning Outcomes Score

knitr::include_graphics("Outputs/Figures_Corpus_individual/LearningOutcomes.svg")
Relationships between density of valuation studies and associated institutions, and learning outcomes

Relationships between density of valuation studies and associated institutions, and learning outcomes

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/LearningOutcomes_log.svg")
Log relationships between density of valuation studies and associated insitutions, and learning outcomes

Log relationships between density of valuation studies and associated insitutions, and learning outcomes

GDP

knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP.svg")
Relationships between density of valuation studies and associated institutions, and GDP

Relationships between density of valuation studies and associated institutions, and GDP

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP_log.svg")
LLog relationships between density of valuation studies and associated insitutions, and GDP

LLog relationships between density of valuation studies and associated insitutions, and GDP

Corruption Perception Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/CPI.svg")
Relationships between density of valuation studies and associated institutions, and the corruption perception index

Relationships between density of valuation studies and associated institutions, and the corruption perception index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/CPI_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the corruption perception index

Log relationships between density of valuation studies and associated insitutions, and the corruption perception index

Population

knitr::include_graphics("Outputs/Figures_Corpus_individual/POP.svg")
Relationships between density of valuation studies and associated institutions, and population

Relationships between density of valuation studies and associated institutions, and population

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/POP_log.svg")
Log relationships between density of valuation studies and associated insitutions, and population

Log relationships between density of valuation studies and associated insitutions, and population

GDP per capita

knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP_per_capita.svg")
Relationships between density of valuation studies and associated institutions, and GDP per capita

Relationships between density of valuation studies and associated institutions, and GDP per capita

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP_per_capita_log.svg")
Log relationships between density of valuation studies and associated insitutions, and GDP per capita

Log relationships between density of valuation studies and associated insitutions, and GDP per capita

Forest Area Under FSC Certification

knitr::include_graphics("Outputs/Figures_Corpus_individual/FSC.svg")
Relationships between density of valuation studies and associated institutions, and forest area under FSC certification

Relationships between density of valuation studies and associated institutions, and forest area under FSC certification

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/FSC_log.svg")
Log relationships between density of valuation studies and associated insitutions, and forest area under FSC certification

Log relationships between density of valuation studies and associated insitutions, and forest area under FSC certification

Biodiversity Habitat Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_habitat_index.svg")
Relationships between density of valuation studies and associated institutions, and the biodiversity habitat index

Relationships between density of valuation studies and associated institutions, and the biodiversity habitat index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_habitat_index_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the biodiversity habitat index

Log relationships between density of valuation studies and associated insitutions, and the biodiversity habitat index

Biodiversity Intactness Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_intactness_index.svg")
Relationships between density of valuation studies and associated institutions, and the biodiversity intactness index

Relationships between density of valuation studies and associated institutions, and the biodiversity intactness index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_intactness_index_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the biodiversity intactness index

Log relationships between density of valuation studies and associated insitutions, and the biodiversity intactness index

Biocapacity Per Capita

knitr::include_graphics("Outputs/Figures_Corpus_individual/Biocapacity_per_capita.svg")
Relationships between density of valuation studies and associated institutions, and the biocapacity per capita

Relationships between density of valuation studies and associated institutions, and the biocapacity per capita

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Biocapacity_per_capita_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the biocapacity per capita

Log relationships between density of valuation studies and associated insitutions, and the biocapacity per capita

Ecological Footprint Per Capita

knitr::include_graphics("Outputs/Figures_Corpus_individual/Ecological_footprint.svg")
Relationships between density of valuation studies and associated institutions, and the ecological footprint per capita

Relationships between density of valuation studies and associated institutions, and the ecological footprint per capita

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Ecological_footprint_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the ecological footprint per capita

Log relationships between density of valuation studies and associated insitutions, and the ecological footprint per capita

Forest Area

knitr::include_graphics("Outputs/Figures_Corpus_individual/Forest_area.svg")
Relationships between density of valuation studies and associated institutions, and forest area

Relationships between density of valuation studies and associated institutions, and forest area

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Forest_area_log.svg")
Log relationships between density of valuation studies and associated insitutions, and forest area

Log relationships between density of valuation studies and associated insitutions, and forest area

Water Footprint

knitr::include_graphics("Outputs/Figures_Corpus_individual/Water_footprint.svg")
Relationships between density of valuation studies and associated institutions, and the water footprint

Relationships between density of valuation studies and associated institutions, and the water footprint

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Water_footprint_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the water footprint

Log relationships between density of valuation studies and associated insitutions, and the water footprint

Inland Fishery Production

knitr::include_graphics("Outputs/Figures_Corpus_individual/Inland_fishery_production.svg")
Relationships between density of valuation studies and associated institutions, and inland fishery production

Relationships between density of valuation studies and associated institutions, and inland fishery production

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Inland_fishery_production_log.svg")
Log relationships between density of valuation studies and associated insitutions, and inland fishery production

Log relationships between density of valuation studies and associated insitutions, and inland fishery production

Region-based Marine Trophic Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/Marine_tropic_index.svg")
Relationships between density of valuation studies and associated institutions, and the region-based marine trophic index

Relationships between density of valuation studies and associated institutions, and the region-based marine trophic index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Marine_tropic_index_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the region-based marine trophic index

Log relationships between density of valuation studies and associated insitutions, and the region-based marine trophic index

Nitrogen Fertilizers

knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_fertilizers.svg")
Relationships between density of valuation studies and associated institutions, and nitrogen fertization

Relationships between density of valuation studies and associated institutions, and nitrogen fertization

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_fertilizers_log.svg")
Log relationships between density of valuation studies and associated insitutions, and nitrogen fertization

Log relationships between density of valuation studies and associated insitutions, and nitrogen fertization

Percent Nitrogen Use Efficiency

knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_use_efficiency.svg")
Relationships between density of valuation studies and associated institutions, and nitrogen use efficiency

Relationships between density of valuation studies and associated institutions, and nitrogen use efficiency

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_use_efficiency_log.svg")
Log relationships between density of valuation studies and associated insitutions, and nitrogen use efficiency

Log relationships between density of valuation studies and associated insitutions, and nitrogen use efficiency

Percentage of Area Covered by Protected Areas

knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_protected.svg")
Relationships between density of valuation studies and associated institutions, and percentage of area covered by protected areas

Relationships between density of valuation studies and associated institutions, and percentage of area covered by protected areas

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_protected_log.svg")
Log relationships between density of valuation studies and associated insitutions, and percentage of area covered by protected areas

Log relationships between density of valuation studies and associated insitutions, and percentage of area covered by protected areas

Percentage of Undernourished People

knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_of_undernourished_people.svg")
Relationships between density of valuation studies and associated institutions, and percentage of undernourished people

Relationships between density of valuation studies and associated institutions, and percentage of undernourished people

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_of_undernourished_people_log.svg")
Log relationships between density of valuation studies and associated insitutions, and percentage of undernourished people

Log relationships between density of valuation studies and associated insitutions, and percentage of undernourished people

Local Breeds at Risk of Extinction

knitr::include_graphics("Outputs/Figures_Corpus_individual/Local_breeds.svg")
Relationships between density of valuation studies and associated institutions, and local breeds at risk of extinction

Relationships between density of valuation studies and associated institutions, and local breeds at risk of extinction

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Local_breeds_log.svg")
Log relationships between density of valuation studies and associated insitutions, and local breeds at risk of extinction

Log relationships between density of valuation studies and associated insitutions, and local breeds at risk of extinction

Percentage of Key Biodiversity Areas Protected

knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_of_key_biodiversity_area_coverage.svg")
Relationships between density of valuation studies and associated institutions, and PA of key biodiverisy area coverage

Relationships between density of valuation studies and associated institutions, and PA of key biodiverisy area coverage

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_of_key_biodiversity_area_coverage_log.svg")
Log relationships between valuation atlas and PA of key biodiverisy area coverage

Log relationships between valuation atlas and PA of key biodiverisy area coverage

Protected Area Management Effectiveness

knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_management_effectiveness.svg")
Relationships between density of valuation studies and associated institutions, and protected area management effectiveness

Relationships between density of valuation studies and associated institutions, and protected area management effectiveness

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_management_effectiveness_log.svg")
Log relationships between valuation atlas and protected area management effectiveness

Log relationships between valuation atlas and protected area management effectiveness

Protected Area Connectedness Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_connectedness.svg")
Relationships between density of valuation studies and associated institutions, and the protected area connectedness index

Relationships between density of valuation studies and associated institutions, and the protected area connectedness index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_connectedness_log.svg")
Log relationships between valuation atlas and the protected area connectedness index

Log relationships between valuation atlas and the protected area connectedness index

Species Habitat Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_habitat_index.svg")
Relationships between density of valuation studies and associated institutions, and the species habitat index

Relationships between density of valuation studies and associated institutions, and the species habitat index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_habitat_index_log.svg")
Log relationships between valuation atlas and the species habitat index

Log relationships between valuation atlas and the species habitat index

Species Protection Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/species_protection_index.svg")
Relationships between density of valuation studies and associated institutions, and the species protection index

Relationships between density of valuation studies and associated institutions, and the species protection index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/species_protection_index_log.svg")
Log relationships between valuation atlas and the species protection index

Log relationships between valuation atlas and the species protection index

Species Status Information Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_status.svg")
Relationships between density of valuation studies and associated institutions, and the species status information index

Relationships between density of valuation studies and associated institutions, and the species status information index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_status_log.svg")
Log relationships between valuation atlas and the species status information index

Log relationships between valuation atlas and the species status information index

Total Wood Removals

knitr::include_graphics("Outputs/Figures_Corpus_individual/Total_wood_removals.svg")
Relationships between density of valuation studies and associated institutions, and total wood removals

Relationships between density of valuation studies and associated institutions, and total wood removals

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Total_wood_removals_log.svg")
Log relationships between valuation atlas and total wood removals

Log relationships between valuation atlas and total wood removals

5 References

Datasets

Altinok, N., N. Angrist and H.A. Patrinos. 2018. “Global data set on education quality (1965-2015).” World Bank Policy Research Working Paper No. 8314. Washington, DC. https://ourworldindata.org/grapher/learning-outcomes-vs-gdp-per-capita

Corruption Perception Index (2020) by Transparency International is licensed under CC-BY-ND 4.0 https://www.transparency.org/en/cpi/2020/index/

FAO. FAOSTAT. Annual Population. Latest update: 16/12/2019. (Accessed 24/03/2021). http://www.fao.org/faostat/en/#data/OA

FAO. FAOSTAT. Macro Indicators: Gross Domestic Product - Values US$. Latest update: 27/04/2021. (Accessed 02/09/2021). http://www.fao.org/faostat/en/#data/MK

Global Administrative Areas (version 3.6). University of California, Berkeley. Available online: https://gadm.org/

IPBES task force on knowledge and data (2020) IPBES Core Indicators - Harmonized Dataset, technical support unit on knowledge and data, Frankfurt, Germany. DOI: 10.5281/zenodo.4439411

IPBES Technical Support Unit on Knowledge and Data. (2020). IPBES regions and sub-regions (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3928281

ISO 3166-1:2020, Codes for the representation of names of countries and their subdivisions — Part 1: Country codes

United Nations Development Programme. Human Development Reports. Human Development Index (HDI). (Accessed 24/03/2021). http://hdr.undp.org/en/indicators/137506#

R packages

Claus O. Wilke (2019). cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. R package version 1.0.0. https://CRAN.R-project.org/package=cowplot

Frank E Harrell, et al. (2020). Hmisc: Harrell Miscellaneous. R package version 4.4-1. https://CRAN.R-project.org/package=Hmisc

Hadley Wickham and Jennifer Bryan (2019). readxl: Read Excel Files. R package version 1.3.1. https://CRAN.R-project.org/package=readxl

Michael C. J. Kao, Markus Gesmann and Filippo Gheri (2020). FAOSTAT: Download Data from the FAOSTAT Database. R package version 2.2.1. https://CRAN.R-project.org/package=FAOSTAT

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

Wei T, Simko V (2021). R package ‘corrplot’: Visualization of a Correlation Matrix. (Version 0.90), https://github.com/taiyun/corrplot.

Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686